1 Learning Objectives

• Understand why storytelling is important in data
  
• Know what different things to consider when telling a data story
  
• Understand what factors make engaging visualisations and stories

2 What is Data Storytelling?

Contrary to popular belief, data storytelling is not simply data visualization.

Instead, it is the process of creating a story out of data analysis findings, allowing regular business people and non-technical staff to understand the possibly complex insights and apply them to a business decision or action. And we need this: data storytelling allows us to extract and communicate insight through compelling stories.

And interestingly, the ability to make sense of large volumes of messy data and find unique insights in them is one of the hottest skills in the job market, according to LinkedIn.

Data visualization expert Stephen Few said:

“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” Any insight worth sharing is probably best shared as a data story. The phrase “data storytelling” has been associated with many things—data visualizations, infographics, dashboards, data presentations, and so on. Too often data storytelling is interpreted as just visualizing data effectively, however, it is much more than just creating visually-appealing data charts. Data storytelling is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative.

For example, take a look at the following graphs:

Task - 2 mins

What do you think about the above graphs? Do they tell you a story? Why do you think they are effective or ineffective?

3 Why do we need to learn about Storytelling?

For some people, crafting a story around the data may seem like an unnecessary, time-consuming effort. They may feel the insights or facts should be sufficient to stand on their own as long as they’re reported in a clear manner. They may believe the revealed insights alone should influence the right decisions and drive their audience to act.

Unfortunately, this point of view is based on the flawed assumption that business decisions are based solely on logic and reason. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. This is where the storytelling aspect comes in: if you are able to take data and tell a story about it to anyone, you’ll be successful and helpful to businesses. When you package up your insights as a data story, you build a bridge for your data to be portrayed accurately, but also so that people who have to use it to make business decisions can understand it. The highest-value content provides insight, rather than just knowledge. And the key thing is insight helps decision-making, and is therefore the most meaningful.

Task - 5 mins

What do you think the hardest part of storytelling might be?

Potential Answers

Insights are often hidden: Data insights don’t come to us naturally. We need to seek them out. And as you know, datasets are often very messy, the key variables might be in disparate locations, data be stored in a few different files, or only accessible on a tool we don’t know how to use. Before we can use data we need to get it out into the open where we can see it. And we need to be able to use our data knowledge to extract these insights first and figure out how do display them. All before we even get to our story

Data is often complicated to understand: There is a reason why many universities offer extended degree programs in data, including Data Management and related programs. You need a specialized skill set to be able to understand data concepts at their base level.

Data on it’s own isn’t actionable: When was the last time you looked at an Excel spreadsheet full of reams and reams of data, slapped your head and said “Why didn’t I think of that before!” Probably never, right? Data, absent the proper context, doesn’t give you what you need to make better decisions and take action.

Creating a compelling story is difficult: It’s hard to get people to pay attention. It’s hard to keep people engaged. Coming up with a way to present your data that is clear, concise, accurate and interesting is a tough skill to master.

Hopefully you can start to see the usefulness in carefully thinking about the story you tell with data. In today’s session, we will cover different aspects of how to do this. For this, there are different components, and it’s important to understand how these different elements combine and work together in data storytelling:

• How do we create an effective story
  
• How do we make effective graphs to get our insights across
  
• How to create effective narratives to go alongside our data

4 Creating your story

There are a few things to think about before you start with your presentation.

Goal: Before you create anything or even come up with an idea, you should always know what your goal is. This is just as important to data storytelling as anything else. Are you trying to answer a specific question asked by a business? Are you trying to convince colleagues to take action on something? Are you trying to show data to get a project funded? Whatever your goal, it’s important to write it down first so you can plan accordingly.

Story: An effective data story isn’t just a smattering of stats. If you’ve done your job well and teased out the insights, you can craft a narrative to reveal that story. That said, the story you’re trying to tell will very much dictate the data visualization format you choose. Is there a clear message? Are people meant to uncover or assign their own meaning to the story? Are you creating a useful resource? Are you trying to guide them to a next action? Keep this in mind.

Volume of data: This is one of the biggest issues we see over and over. People can go data-crazy sometimes and extract a mountain of data to turn into a story. That’s nice, but you must consider how much data is really required to tell the story. Sometimes you have millions of data points, and there is no need to use it all.

Audience: Who are you trying to reach with your data story? Who will be interested in it? What level of knowledge or understanding do they have? What data visualization formats are they accustomed to interacting with?

Once you’ve got a plan, you need to fill your story. What makes good visualisations?

5 Creating your visuals

For data, the visual and auditory human perceptual system are probably the most important: you will either be creating charts and dashboards that people will look at, writing reports that people will read, or giving presentations that people will be listening to.

Let’s start with vision. Contrary to what you may have learned in high school, the process of perceiving an object is much more complex than the conceptual model of a digital video camera, in which our eyes act as a lens, our optic nerves as cables and our brains as processors and hard drives. Human vision is much more complex than just processing the light that is reflected by an object.

6 Creating a narrative

Once you have your overall goal, visualisations, and general story, you need to work on your narrative. You’ll see some of the best examples of modern-day storytelling during the popular TED conference series. Analysis of the most popular 500 TED Talk presentations found that stories made up at least 65% of their content. Throughout time, storytelling has proven to be a powerful delivery mechanism for sharing insights and ideas in a way that is memorable, persuasive, and engaging. However, extracting the most value out of data isn’t always easy. And that’s why we need to learn this skill.

Task - 10 mins

Making stats exciting…

Click on the image to watch the video below. This is world famous statistics expert Hans Rosling presenting UN statistics data about world development over time. Start at 2.30 minutes in and watch until 5 minutes in.

Get in a group and discuss:

• Did you like the presentation more or less than you thought you would, given that it was about UN statistics data?
  
• What was good about this?
  
• What wasn’t so good?
  
• Do you feel like he told a story with the data? If so, how did he achieve this?
  
• Are there any techniques he’s used that particularly stand out to you?

The key take home really is that in order to make data insights effective, you need to make people pay attention to, and understand it. This is where a really good narrative can come in: it helps people to engage, and more importantly remember the message you are giving.

Keep in mind that data storytelling is not a story about numbers; good data alone does not make a good data story. Data storytelling is only effective when it provides value, whether it teaches people something new, gives them a new perspective, or inspires them to take action.

7 Storytelling : task

Let’s have a quick practice of everything we’ve learned so far. We’re going to take a dataset, do a simple analysis on it, and create a “story”.

Task - 30 mins

Your business question: Have lego sets got bigger over time?

Your context: you work for lego, and the CEO wants the answer to this question so he can make decisions about how much lego to include in the next set. He has no numerate skills whatsoever, no data expertise, and no coding skills, so you should present to him accordingly. He only wants to see one graph, and hear a meaningful answer to his question. You are allowed to give a two minute presentation to answer his question.